code: def test_deploy_with_input_handlers(
      sagemaker_session,
      instance_type,
      tf_full_version,
      tf_full_py_version,
  ):
      estimator = TensorFlow(
          entry_point="training.py",
          source_dir=TFS_RESOURCE_PATH,
          role=ROLE,
          instance_count=1,
          instance_type=instance_type,
          framework_version=tf_full_version,
          py_version=tf_full_py_version,
          sagemaker_session=sagemaker_session,
          tags=TAGS,
      )
  
      estimator.fit(
          job_name=unique_name_from_base("test-tf-tfs-deploy")
      )
  
      endpoint_name = estimator.latest_training_job.name
  
      with timeout.timeout_and_delete_endpoint_by_name(
          endpoint_name, sagemaker_session
      ):
          predictor = estimator.deploy(
              initial_instance_count=1,
              instance_type=instance_type,
              endpoint_name=endpoint_name,
              entry_point=os.path.join(
                  TFS_RESOURCE_PATH, "inference.py"
              ),
          )
  
          input_data = {"instances": [1.0, 2.0, 5.0]}
          expected_result = {"predictions": [4.0, 4.5, 6.0]}
  
          result = predictor.predict(input_data)
          assert expected_result == result
  
imports: from __future__ import absolute_import
  import numpy
  import os
  import time
  import pytest
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.tensorflow import TensorFlow, TensorFlowProcessor, TensorFlowModel
  from sagemaker.utils import unique_name_from_base, sagemaker_timestamp
  import tests.integ
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, kms_utils, timeout
  from tests.integ.retry import retries
  from tests.integ.utils import gpu_list, retry_with_instance_list
  from tests.integ.s3_utils import assert_s3_file_patterns_exist
  from packaging.version import Version
label_desc: Use image node_id 9 for calling a global function with example usage: test_deploy_with_input_handlers(sagemaker_session, instance_type, tf_full_version, tf_full_py_version) without return types